Automatic detection of certain content in images and/or other forms of data is of ever-increasing importance for machine vision, security, computer-aided diagnosis and other applications. For example, automated detection of anatomic structures is an important functionality for navigating through large 3D image datasets and supporting computer-aided diagnosis (CAD).
A classifier is a mechanism that can be used to perform automatic detection in such applications. Once trained, a classifier can indicate whether an image includes a certain object, such as an anatomic structure. Based on the amount of training, a classifier can exhibit a better or worse performance. With an on-line classifier, training may be performed during normal use of the classifier. Because of this ability to train during normal use, and hence continually improve performance while being used, on-line classifiers are increasing in popularity.
However, current on-line classifiers lack adaptations for dealing with training data sets where an imbalance exists between the proportions of true-positive, true-negative, false-positive, and false-negative samples. Furthermore, current on-line classifiers are unable to adapt to shifts in the proportions of positive and negative samples that occur as the sizes of training data sets expand over time.
Accordingly, new mechanisms for updating a classifier are desirable.